ChatGPT Changes

Dear Readers,

Who would have thought that the “smartest model ever” would trigger one of the loudest user revolts in AI history? The return of GPT-4o after only 24 hours shows how attached people are to the personality of their AI—and how quickly trust crumbles when expectations are not met. In this issue, we not only look at OpenAI's response, but also at how the balance of power between developers and the community is shifting.

We also take you on a journey through the tectonic shifts in the financial world: Hedge funds are automating up to 75% of traditional analyst work, central banks are discussing cautious interest rate policy in the AI era, and analysts are warning of a US economic slowdown despite the AI boom. Plus, there are benchmarks, market trends, and a few sharp jabs from the xAI camp. An issue full of dynamism, insights, and food for thought.

In Today’s Issue:

All the best,

It only took 24 hours for OpenAI to pull the emergency brake and bring GPT-4o back to life. What happened?

The launch of ChatGPT-5 was supposed to be a triumph: the smartest, fastest, and most useful model ever, as OpenAI boldly promised. But instead of cheers, Sam Altman got an unprecedented backlash.

Reddit users called GPT-5 “garbage,” complained about shorter, less helpful responses, and missed their familiar models.

The problem? A broken auto-switch system that made GPT-5 appear significantly dumber, combined with drastically reduced rate limits that frustrated even paying Plus users.

For the AI community, this episode reveals fascinating insights: users develop emotional attachments to AI personalities, technical perfection alone is not enough, and even market leaders must respond quickly to dissatisfied customers. Altman's response was remarkably transparent – he doubled the rate limits from 200 to 3,000 per week, explained technical details, and brought back old models.

Doesn't this controversy show us that AI development is increasingly becoming a dialogue between developers and the community? What lessons can other AI companies learn from OpenAI's “bumpy” launch?

Why it matters: This controversy shows that even leading AI companies must expect unexpected user reactions when introducing new models. It also highlights the importance of transparent communication and rapid adjustments for maintaining the trust of the AI community.

Sources:

🔗  https://www.tomsguide.com/ai/chatbots/chatgpt-4o-is-coming-back-after-massive-gpt-5-backlash

🔗 https://techcrunch.com/2025/01/12/sam-altman-addresses-bumpy-gpt-5-rollout/

🔗 https://venturebeat.com/ai/openai-returns-old-models-to-chatgpt-as-sam-altman-admits-bumpy-gpt-5-rollout/

🔗 https://www.bleepingcomputer.com/news/technology/openai-to-fix-gpt-5-issues-double-rate-limits-for-paid-users-after-outrage/

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Hedge funds and research teams are automating up to 75% of traditional analyst work (DCF, screening, CRM) with LLMs; productivity is quadrupling, and hiring is shifting to client-facing roles. Quants are not immune (e.g., AlphaGPT). This is a turning point for cost structures, margins, and data moats in asset management; committee decisions will be partly AI-supported. Implication: The industry's headcount mix, wages, and unit economics are undergoing structural change.

Fed representatives (including Lisa D. Cook and Susan M. Collins) emphasize AI as a general-purpose technology with productivity gains, changing job tasks, and potential effects on inflation.

Political conclusion: The data is shaky, so calibration should be “cautious and humble” while companies realize initial efficiency gains (e.g., fewer production errors). Implication: AI may dampen price and wage dynamics in the medium term, but policy will respond gradually.

BCA Research sees a 60% risk of recession in the US despite AI euphoria: capex leakage abroad (chips), weak tech employment, rising electricity prices due to data centers, meager productivity effects to date, and soft broad indicators. Implication: Stocks may play the AI story, but the real economy and policymakers should not rely on short-term AI rescue; energy/grid bottlenecks are becoming a macro issue.

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